Information technology and the World Wide Web (WWW) have made entering into relationships and transacting business with remote entities an everyday occurrence. Today, activities ranging from signing up for a university course, purchasing a product, selling a product, and screening job applicants are conducted through the Internet, with no face-to-face contact between the parties to the activities. Not only does one not see the person or business at the other end of the transaction, but one rarely has the opportunity to assess the reliability of that person or business. It may be time-consuming and costly to conduct an investigation.
Rating systems, in particular on-line rating systems, are known in the art and are becoming increasingly popular. These rating systems provide a forum for people to rate products, businesses, publications, music, and people. They follow a standard methodology: a user is presented with a person/object to rate and a rating spectrum. The user selects a rating somewhere within the spectrum and that rating is applied to the person/object's current rating. The drawback to these systems is that they are often not reliable because they fail to take into account various factors which may affect a rating. There is no attempt made to rate the raters to determine if a rater is reliable or even real.
Many of these rating systems employ very rudimentary algorithms for determining a rating score. Most simply provide an average score of all ratings, without taking into consideration factors such as raters creating fake identities and posting SPAM ratings for the purpose of inflating the average rating of their friends and/or reducing the average rating of their competitors. Moreover, with many of these systems there is no way to compensate for the age of a rating. For instance, using the example of www.RateMyProfessors.com, assume a teacher was given poor ratings years ago (a two on a scale of one to five). Years later the teacher is receiving consistently favorable ratings, but the favorable ratings are weighted down by the unfavorable ratings posted years ago. With all of the ratings having equal weight, it will take a high volume of current ratings to compensate for the older unfavorable ratings. This builds unfairness and lack of accuracy into the system and also brings up another problem with known rating systems—they perform poorly with few postings because of the bias and time factors.
Therefore there is a need for a method that takes into consideration the factors discussed above to overcome the shortcomings of the prior art.